In most college and university libraries, materials published before 1800 have been moved into special collections, while the post-1923 books remain in general circulation. But books published between these dates are vulnerable to deaccessioning, as libraries increasingly reconfigure access to public-domain texts via digital repositories such as Google Books. Even libraries with strong commitments to their print collections are clearing out the duplicates, assuming that circulating copies of any given nineteenth-century edition are essentially identical to one another. When you look closely, however, you see that they are not. Many nineteenth-century books were donated by alumni or their families decades ago, and many of them bear traces left behind by the people who first owned and used them. In Book Traces, Andrew M. Stauffer adopts what he calls "guided serendipity" as a tactic in pursuit of two goals: first, to read nineteenth-century poetry through the clues and objects earlier readers left in their books and, second, to defend the value of keeping the physical volumes on the shelves. Finding in such books of poetry the inscriptions, annotations, and insertions made by their original owners, and using them as exemplary case studies, Stauffer shows how the physical, historical book enables a modern reader to encounter poetry through the eyes of someone for whom it was personal.

The authors of the pioneering Cutting-Edge Marketing Analytics return to the vital conversation of leveraging big data with Marketing Analytics: Essential Tools for Data-Driven Decisions, which updates and expands on the earlier book as we enter the 2020s. As they illustrate, big data analytics is the engine that drives marketing, providing a forward-looking, predictive perspective for marketing decision-making. The book presents actual cases and data, giving readers invaluable real-world instruction. The cases show how to identify relevant data, choose the best analytics technique, and investigate the link between marketing plans and customer behavior. These actual scenarios shed light on the most pressing marketing questions, such as setting the optimal price for one’s product or designing effective digital marketing campaigns. Big data is currently the most powerful resource to the marketing professional, and this book illustrates how to fully harness that power to effectively maximize marketing efforts.

WINNER: The 2018 McGannon Center Book Prize and shortlisted for the Goddard Riverside Stephan Russo Book Prize for Social Justice The New York Times Book Review: "Riveting." Naomi Klein: "This book is downright scary." Ethan Zuckerman, MIT: "Should be required reading." Dorothy Roberts, author of Killing the Black Body: "A must-read." Astra Taylor, author of The People's Platform: "The single most important book about technology you will read this year." Cory Doctorow: "Indispensable." A powerful investigative look at data-based discrimination—and how technology affects civil and human rights and economic equity The State of Indiana denies one million applications for healthcare, foodstamps and cash benefits in three years—because a new computer system interprets any mistake as “failure to cooperate.” In Los Angeles, an algorithm calculates the comparative vulnerability of tens of thousands of homeless people in order to prioritize them for an inadequate pool of housing resources. In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect. Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor. In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile. The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values. This deeply researched and passionate book could not be more timely.